regul.adj {pastecs} | R Documentation

## Adjust regulation parameters

### Description

Calculate and plot an histogram of the distances between interpolated observations in a regulated time series and closest observations in the initial irregular time series. This allows to optimise the `tol` parameter

### Usage

    regul.adj(x, xmin=min(x), frequency=NULL, deltat, tol=deltat,
            tol.type="both", nclass=50, col=c(4, 5, 2), plotit=TRUE, ...)

### Arguments

`x` | a vector with times corresponding to the observations in the irregular initial time series
`xmin` | the time corresponding to the first observation in the regular time series
`frequency` | the frequency of observations in the regular time series
`deltat` | the interval between two successive observations in the regular time series. This is the inverse of `frequency`. Only one of both parameters need to be given. If both are provided, `frequency` supersedes `deltat`
`tol` | the tolerance in the difference between two matching observations (in the original irregular series and in the regulated series). If `tol=0` both values must be strictly identical; a higher value for `tol` allows some fuzzy matching. `tol` must be a round fraction of `deltat` and cannot be higher than it, otherwise, it is adjusted to the closest acceptable value. By default, `tol=deltat`
`tol.type` | the type of window to use for the time-tolerance: `"left"`, `"right"`, `"both"` (by default) or `"none"`. If `tol.type="left"`, corresponding `x` values are seeked in a window ]xregul-tol, xregul]. If `tol.type="right"`, they are seeked in the window [xregul, xregul+tol[. If `tol.type="both"`, then they are seeked in the window ]xregul-tol, xregul+tol]. If several observations are in this window, the closest one is used. Finally, if `tol.type="none"`, then all observations in the regulated time series are interpolated (even if exactly matching observations exist!)
`nclass` | the number of classes to compute in the histogram. This is indicative, and will be adjusted by the algorithm to produce a nicely-formatted histogram. The default value is `nclass=50`. It is acceptable in many cases, but if the histogram is not correct, try a larger value
`col` | the three colors to use to represent respectively the fist bar (exact coincidence), the middle bars (coincidence in a certain tolerance window) and the last bar (values always interpolated). By default, `col=c(4,5,2)`
`plotit` | if `plotit=TRUE` then the histogram is plotted. Otherwise, it is only calculated
`...` | additional graph parameters for the histogram

### Details

This function is complementary to `regul.screen()`. While the later look for the best combination of the number of observations, the interval between observations and the position of the first observation on the time-scale for the regular time series, `regul.adj()` look for the optimal value for `tol`, the tolerance window.

### Value

A list with components:

`params` | the parameters used for the regular time-scale
`match` | the number of matching observations in the tolerance window
`exact.match` | the number of exact matching observations
`match.counts` | a vector with the number of matching observations for increasing values of `tol`

### Author(s)

Philippe Grosjean (phgrosjean@sciviews.org), Frédéric Ibanez (ibanez@obs-vlfr.fr)

### See Also

`regul.screen`, `regul`

### Examples

    # This example follows the example for regul.screen()
    # where we determined that xmin=9, deltat=21, n=63, with tol=1.05
    # is a good choice to regulate the irregular time series in 'releve'
    data(releve)
    regul.adj(releve$Day, xmin=9, deltat=21)
    # The histogram indicates that it is not useful to increase tol
    # more than 1.05, because few observations will be added
    # except if we increase it to 5-7, but this value could be
    # considered to be too large in comparison with deltat=22
    # On the other hand, with tol <= 1, the number of matching
    # observations will be almost divided by two!

[Package pastecs version 1.3-2 Index]